**arXiv ID:** 2205.11636 **Authors:** Bohdan M. Pavlyshenko **Published:** 2022-05-23T21:06:27Z **Abstract:** The paper describes the deep learning approach for forecasting non-stationary time series with using time trend correction in a neural network model. Along with the layers for predicting sales values, the neural network model includes a subnetwork block for the prediction weight for a time trend term which is added to a predicted sales value. The time trend term is considered as a pro...
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# Forecasting of Non-Stationary Sales Time Series Using Deep Learning
**arXiv ID:** 2205.11636
**Authors:** Bohdan M. Pavlyshenko
**Published:** 2022-05-23T21:06:27Z
**Abstract:**
The paper describes the deep learning approach for forecasting non-stationary time series with using time trend correction in a neural network model. Along with the layers for predicting sales values, the neural network model includes a subnetwork block for the prediction weight for a time trend term which is added to a predicted sales value. The time trend term is considered as a product of the predicted weight value and normalized time value. The results show that the forecasting accuracy can be essentially improved for non-stationary sales with time trends using the trend correction block in the deep learning model.
## Skill Description
This skill is generated from the arXiv paper: Forecasting of Non-Stationary Sales Time Series Using Deep Learning (2205.11636).
## How to Use
[To be filled in by the user or by future automation]
## References
- [arXiv:2205.11636](http://arxiv.org/abs/2205.11636v1)
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